You're viewing demo data. Sign in and upload your LinkedIn connections to see jobs where you know someone.
Export from LinkedIn Upload CSV
← Back

Member of Technical Staff - Product (Backend)

Modal · New York · ashby

Apply →
First seen: 2026-02-07 · Last seen: 2026-02-10

Why You're a Fit

Hands-on with LLMs, PyTorch, CUDA, GPU inference
Senior Director, Generative AI, Teradata (2023-2025)
"...mple to train models, run batch jobs, and serve low-latency inference..."
LLMs & Generative AI (PyTorch, CUDA, GPU inference)
Technical skills
"...mple to train models, run batch jobs, and serve low-latency inference..."

Job Description

About Us:

Modal provides the infrastructure foundation for AI teams. With instant GPU access, sub-second container startups, and native storage, Modal makes it simple to train models, run batch jobs, and serve low-latency inference. Companies like Suno, Lovable, and Substack rely on Modal to move from prototype to production without the burden of managing infrastructure.

We're a fast-growing team based out of NYC, SF, and Stockholm. We've hit 9-figure ARR and recently raised a Series B at a $1.1B valuation. We have thousands of customers who rely on us for production AI workloads, including Lovable, Scale AI, Substack, and Suno.

Working at Modal means joining one of the fastest-growing AI infrastructure organizations at an early stage, with many opportunities to grow within the company. Our team includes creators of popular open-source projects (e.g. Seaborn, Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.

The Role:

We're looking for strong backend engineers who love building a developer tools used by the largest AI companies in the world. You’ll be building for things at scale, but also for new AI workflows that change every day.

Requirements:

  • Experience building and shipping modern web applications end-to-end. We care more about what you’ve built than how many years you’ve been building.

  • Comfort working across the stack: TypeScript on the frontend, Python services on the backend, and ClickHouse for data and analytics.

  • Deep knowledge of observability tools and patterns used for large-scale workloads such as custom sandboxes, training and inference for large language (LLM) and diffusion models.

  • Experience with at least one of: billing/payments systems, B2B SaaS tooling, or enterprise software, or LLM / diffusion models inference and training loads.

  • Strong product instincts; you think about customer problems, not just tickets.

  • Ability to make good tradeoffs between shipping fast and building for scale.

  • Ability to work in-person in our NYC office.

Apply for this role →